A Modified Discriminant Sparse Representation Method For Face Recognition

Keywords

Face recognition; L2 regularization; Sparse Representation

Abstract

Recently, a new discriminative sparse representation method for robust face recognition that uses ℓ2-norm regularization was reported. In this paper, direct data-driven calculation of the balance parameter used in the objective function is presented. The modified system preserves the advantages of the original method while improving the recognition accuracy and making the system more automated, i.e., less dependent on the user's input. Extensive simulations are performed on six face databases, namely, ORL, YALE, FERET, FEI, Cropped AR, and Georgia Tech. Sample results are given demonstrating the properties of the modified system.

Publication Date

2-22-2018

Publication Title

2018 IEEE 8th Annual Computing and Communication Workshop and Conference, CCWC 2018

Volume

2018-January

Number of Pages

727-730

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/CCWC.2018.8301679

Socpus ID

85047416293 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85047416293

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